Do Artificial Intelligence Ethical Anxiety, Perceived Ethical Risks and Ethical Awareness Affect College Students' Use of Generative Artificial Intelligence Products-- Research from an Ethical Perspective
Bibliographic record
Abstract
This study aims to explore the factors influencing college students' behavioral intentions (BI) and usage behaviors (UB) of using Generative AI products from an ethical perspective. Based on ethical decision-making theory, the research extends the UTAUT2 model and introduces three key variables: ethical awareness (EA), perceived ethical risk (PER), and AI ethical anxiety (AIEA). The data of 253 college students were analyzed through the partial least squares structural equation model (PLS-SEM).The research results verified the effectiveness of the UTAUT2 model and indicated that performance expectation (PE), hedonic motivation (HM), price value (PV), and social impact (SI) have a positive impact on college students' behavioral intentions to use generative artificial intelligence products, while effort expectation (EE) has no significant effect. Furthermore, convenience conditions (FC) and habits (HB) do not directly affect BI, but they play a decisive role in UB.Among the ethics-related factors, AlEA and PER are not the main determinants of BI, but AIEA can directly inhibit UB. Furthermore, although PER does not directly affect UB, it can have a negative impact indirectly through AIEA. Ethical awareness (EA) can positively influence BI, but it will also increase PER. These findings help to encourage college students to better accept and use generative artificial intelligence products in an ethical manner.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".